Data Analytics using R with Yelp Dataset

•

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Cédric Poottaren

Microsoft Student Partners - Developer's Conference Presentation

Data & Analytics

Data ScienceBig
Processes and systems to extract knowledge or
insights from data
Large and complex data that has been collected
over several years

Dataset
yelp_academic_dataset_business – Business Information
yelp_academic_dataset_review – User Reviews
Combine the 2 using

Text Analytics Methodologies
Natural Language Processing (NLP)

Part 1
Natural
Language
Processing
(NLP)
Microsoft Text Analytics
Sentiment Analysis
Keyword Extraction
Topic Detection
Language Detection

Stemming
Revert the word into its original or root form

Part 2
Natural
Language
Processing
(NLP)
Stanford CoreNLP

Part 2
Natural
Language
Processing
(NLP)
Stanford CoreNLP
Part of Speech
POS Tag Description Example
CC coordinating conjunction and
CD cardinal number 1, third
DT determiner the
EX existential there there is
FW foreign word d’hoevre
IN preposition/subordinating conjunction in, of, like
JJ adjective big
JJR adjective, comparative bigger
JJS adjective, superlative biggest

Term
Document
Matrix
Describes the frequency of terms that occur in a collection of documents

Term
Document
Matrix
Term frequency and weighting
TF-IDF weighting: give higher weight to terms that are
rare

Unsupervised
Learning
K-means Clustering
“Art of finding groups in data” – Kaufman, Rousseeuw
• No clear picture of what is within the document
•
• Natural pair of groupings
•
• Simple to run

Conclusion
1. Data Manipulation in R
2. Natural Language Processing
3. Machine Learning
4. Visualization

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1. Text Analytics on Dataset #DevConMru

2. Data ScienceBig Processes and systems to extract knowledge or insights from data Large and complex data that has been collected over several years

3. What is yelp ?

4. Dataset yelp_academic_dataset_business – Business Information yelp_academic_dataset_review – User Reviews Combine the 2 using

5. DEMO

6. Text Analytics Methodologies Natural Language Processing (NLP)

7. Part 1 Natural Language Processing (NLP) Microsoft Text Analytics Sentiment Analysis Keyword Extraction Topic Detection Language Detection

8. DEMO

9. Stemming Revert the word into its original or root form

10. Stemming (Results)

11. Common Words Removal

12. DEMO

13. Part 2 Natural Language Processing (NLP) Stanford CoreNLP

14. Part 2 Natural Language Processing (NLP) Stanford CoreNLP Part of Speech POS Tag Description Example CC coordinating conjunction and CD cardinal number 1, third DT determiner the EX existential there there is FW foreign word d’hoevre IN preposition/subordinating conjunction in, of, like JJ adjective big JJR adjective, comparative bigger JJS adjective, superlative biggest

15. DEMO

16. Part 2 Natural Language Processing (NLP) Stanford CoreNLP

17. Part 2 Natural Language Processing (NLP) Stanford CoreNLP

18. DEMO

19. Term Document Matrix Describes the frequency of terms that occur in a collection of documents

20. Term Document Matrix Term frequency and weighting TF-IDF weighting: give higher weight to terms that are rare

21. Unsupervised Learning K-means Clustering “Art of finding groups in data” – Kaufman, Rousseeuw • No clear picture of what is within the document • • Natural pair of groupings • • Simple to run

22. K-means Clustering

23.

24. DEMO

25. Conclusion 1. Data Manipulation in R 2. Natural Language Processing 3. Machine Learning 4. Visualization

26. Text Analytics on Dataset Thank you.

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